Rising trend and regional disparities of the global burden of disease attributable to ambient low temperature, 1990-2019: An analysis of data from the Global Burden of Disease 2019 study

Background Previous studies on the effect of global warming on the global burden of disease have mainly focussed on the impact of high temperatures, thereby providing limited evidence of the effect of lower temperatures. Methods We adopted a three-stage analysis approach using data from the Global Burden of Disease 2019 study. First, we explored the global burden of disease attributable to low temperatures, examining variations by gender, age, cause, region, and country. Second, we analysed temporal trends in low-temperature-related disease burdens from 1990 to 2019 by meta-regression. Finally, we fitted a mixed-effects meta-regression model to explore the effect modification of country-level characteristics. Results In 2019, low temperatures were responsible for 2.92% of global deaths and 1.03% of disability-adjusted life years (DALYs), corresponding to a death rate of 21.36 (95% uncertainty interval (UI) = 18.26, 24.73) and a DALY rate of 335 (95% UI = 280, 399) per 100 000 population. Most of the deaths (85.12%) and DALYs (94.38%) attributable to low temperatures were associated with ischaemic heart disease, stroke, and chronic obstructive pulmonary disease. In the last three decades, we observed an upward trend for the annual number of attributable deaths (P < 0.001) and a downward trend for the rates of death (P < 0.001) and DALYs (P < 0.001). The disease burden associated with low temperatures varied considerably among regions and countries, with higher burdens observed in regions with middle or high-middle socio-demographic indices, as well as countries with higher gross domestic product per capita and a larger proportion of ageing population. Conclusions Our findings emphasise the significance of raising public awareness and prioritising policies to protect global population health from the adverse effects of low temperatures, even in the face of global warming. Particular efforts should be targeted towards individuals with underlying diseases (e.g. cardiovascular diseases) and vulnerable countries or regions (e.g. Central Asia and central Europe).


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Table S1.Global burden of all causes attributable to low and high temperature in 2019.

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Table S2.Twelve leading causes of total death rates attributable to low temperatures in 2019 by region.

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Table S3.Twelve leading causes of total DALY rates attributable to low temperatures in 2019 by region.

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Table S4.Global age-standardized rates of death and DALY attributable to high temperature from 1990-2019. 1

Method S1: Estimation of disease burden related to low temperatures
As for estimating low-temperature attributable burden of disease, similar to conventional risk factors, there are mainly four steps: (1) Determination of the inclusion of low temperatures -outcome pairs and data collection: the World Cancer Research Fund criteria for convincing or probable evidence of risk-outcome pairs were used to determine which risk-outcome pair such as low temperatures and all causes mortality are of value for analysis.When pairs are identified, data is collected for further calculations.Take low temperatures as an example.Hourly temperature estimates for each location (at the county or municipality level) were collected from ERA5, a gridded reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts with 0.25°×0.25°spatialand subdaily temporal resolutions, including uncertainty estimates on a 0.5°×0.5°spatial and three-hourly resolution, and then daily mean temperature were calculated.
Individual death information was obtained from the GBD cause of death(CoD) database for vital registration data sources.After linking cause-specific mortality to daily mean temperature estimates, the GBD study modeled relative risks (RR) for different causes of death using a two-dimensional spline within a Bayesian meta-regression framework, specifically MR-BRT (meta-regression-Bayesian, regularized, trimmed) tool.This allowed the GBD study to identify causes with a positive risk-outcome score, indicating an association with temperature.Only causes with a risk-outcome score greater than zero were considered to be cold-related and were included in subsequent analyses.
Therefore, seven disease causes (Level 2) were identified as outcomes related to low temperatures (Figure S2).
(2) Estimation of the population attributable fraction (PAF): When identifying low-temperature-related causes by MR-BTR, meta-analyses of relative risks of low temperatures were also conducted to construct a function of low temperatures (exposure-response curves) based on daily mean temperature and temperature zone.
Modelling along different temperature zones and integrating data from all locations into one model contributed to stabilizing the estimates across zones.Then spatiotemporal Gaussian process regression was used to estimate mean levels of exposure by age-sexlocation-year, and a measure of dispersion such as standard deviation (SD) was modelled to estimate the distribution of exposure across individuals.After that, the theoretical minimum risk level (TMREL) for temperature, defined as the low point of the risk function, was estimated for each given location and year, defining low temperatures as values below TMREL.Since TMREL varies considerably with locations (e.g., higher in hot areas than colder regions), years, and diseases, spatially and temporally varying TMREL was employed to future account for regional heterogeneity.At last, PAF can be calculated by the standard formula with indicators including the exposure levels for low temperature, the RR of the outcome as a function of exposure (exposure-response curves), and counterfactual risk factor exposure (TMREL).
(3) Calculation of low temperatures attributable burden: through PAF, specificcause attributable deaths and DALYs were estimated.The more detailed process has been presented in the website (http://www.healthdata.org/gbd/).
refers to fixed-effects coefficients.The random part of the model, Zitb, indicates the deviation from the averages of outcome in terms of 19 time points and composing the random-effects matrix Zi, with coefficients b.The vector μit defines the unit-level sampling errors.

Figure S2 .
Figure S2.Age-standardized rates of deaths and DALYs attributable to low

Figure S3 .
Figure S3.The number of low temperature-related deaths and DALYs across countries.DALYs=disability-adjusted life-years.

Figure S4 .
Figure S4.Temporal trends in numbers of DALYs and deaths attributable to low

Figure S5 .
Figure S5.Temporal trends of age-standardized DALY and death rates attributable to

Figure S6 .
Figure S6.Correlation between SDI value and age-standardized rate of DALY and

Table S2 . Twelve leading causes of total death rates attributable to low temperatures in 2019 by region. Causes
are ranked according to global estimates of age-standardized death rates (per 100,000 people) provided by the GBD Study 2019.The rates are listed and colored in each cell: Shades of blue indicate death rates less than zero whereas red indicates death rates greater than zero.(Notes: IHD=Ischemic heart disease, COPD=Chronic obstructive pulmonary disease, LRI=Lower respiratory infections, CKD=Chronic kidney disease, DM=Diabetes mellitus, HHD=Hypertensive heart disease, EMF=Exposure to mechanical forces, IV=Interpersonal violence, RI=Road injuries.)

Table S3 . Twelve leading causes of total DALY rates attributable to low temperatures in 2019 by region.
Causes are ranked according to global estimates of age-standardized DALY rates (per 100,000 people) provided by the GBD Study 2019.The rates are listed and colored in each cell: Shades of blue indicate DALY rates less than zero whereas red indicates death rates greater than zero.(Notes: DALY=disability-adjusted life years; IHD=Ischemic heart disease; COPD=Chronic obstructive pulmonary disease; LRI=Lower respiratory infections; CKD=Chronic kidney disease; DM=Diabetes mellitus; HHD=Hypertensive heart disease; EMF=Exposure to mechanical forces; IV=Interpersonal violence; RI=Road injuries.)